September 14–17, 2026
SCDM Annual Conference 2026 — Raleigh
Advancing Clinical Data Management through AI-guided, human-supervised execution. Visit Booth #514.
View event detailsAGENTIC AI IN CLINICAL TRIALS
The First AI WORKFORCE for Clinical Trials
Maxis AI delivers Agentic AI in Life Sciences through the industry's first AI Workforce for Clinical Trials. Built for sponsors, CRO delivery teams, and site networks, it enables governed clinical execution through reasoning, supervised execution, and human oversight across regulated clinical research workflows. Explore the model in our complete guide to AI agents in clinical trials.
Maxis AI delivers Agentic AI in Life Sciences through the industry's first AI Workforce for Clinical Trials. Built for sponsors, CROs, and site networks, it enables governed clinical execution through reasoning, supervised execution, and human oversight across regulated clinical research workflows.Data Mgmt
Safety
Medical
Regulatory
Operations
Quality
TRUSTED ACROSS SPONSORS, CROS & SITE NETWORKS
23
Years industry experience
3,300+
CLINICAL TRIALS
160+
Domain & AI experts
100%
Pharma/LS focus
100%
Renewal rate
169+
AI agents
23+
Orchestrations
100+
System integrations
Enterprise Compliance21 CFR Part 11SOC 2 Type IIISO 27001ISO 9001HIPAAGDPR
A
What is Agentic AI in Life Sciences?
Agentic AI in Life Sciences refers to AI systems capable of observing, reasoning, planning, and executing regulated clinical workflows while operating within governance, auditability, and human oversight.
W
What is an AI Workforce?
An AI Workforce is a coordinated team of governed AI agents that work alongside clinical teams to execute operational workflows across study startup, data management, safety, regulatory submissions, and quality.
The Execution Gap
Visibility is solved. Execution Capacity is the gap.
Most clinical platforms improve visibility. Few increase execution capacity across regulated operations. Clinical teams don't struggle to see risk anymore. They struggle to resolve it at scale. Digital platforms improved monitoring. They did not increase throughput. Maxis introduces an AI Workforce that converts signals into governed execution. Read why this clinical trial execution gap now drives cost, timeline, and compliance risk.
Signal coverage without Maxis AI
0%
Trials with at least one RBM/RBQM component
0%
Execution-stage RBQM adoption
Sources: ACRO RBQM Summary Report; Applied Clinical Trials — Industry Assessment of RBQM.
With Maxis AI Workforce
0%
Signals routed into governed workflows
0%
Actions resolved or escalated within SLA
Why the shift is needed
Structural pressure across clinical development.
Clinical development teams manage increasing protocol complexity, growing data volumes, expanding regulatory expectations, and limited operational capacity. Agentic AI in Clinical Research helps scale execution without proportionally increasing headcount.
Evidence-backed clinical execution pressure indicators
Procedures per visit growth
37%37%
Sites per protocol growth
63%63%
Reporting requirement gaps
51%51%
Phase III data growth
67%67%
Sites impacted by staffing
37%37%
Large studies using 100% SDR
82%82%
“Indicators are drawn from Tufts CSDD, CTTI, ACRO, and clinical research site workforce surveys. They represent related pressure signals, not a single composite index.”
The Solution
An AI Workforce, working alongside your teams.
Clinical SystemsMaxis AI WorkforceGoverned Execution
EDC
CTMS
eTMF
Safety
Labs
Signals
Supervised
Core
Workflow executed
Human approved
Audit trail
Compliant output
Build your own AI Workforce. Explore our latest stack of AI Agents and Integrations.
The shift, measured
AI Workforce Impact Across Clinical Workflows
Across the workflows that bottleneck most studies, supervised execution compresses the cycle without removing oversight. Routine execution moves faster, while teams stay focused on scientific and operational decisions that require human judgment.
Workflow turnaround — before vs. after Maxis AI
Before Maxis AI (weeks) After Maxis AI (hours)
Document Production4–6 weeks11h −95%
Document Distribution & Review4–8 weeks55h −77%
Study Preparation & Cohort Selection4–6 weeks20h −90%
Participant Engagement2–4 weeks24h −80%
Data Integration and Reporting8–12 weeks72h −82%
What an AI Workforce is
A supervised execution layer across regulated workflows.
System of Records
EDC · CTMS · eTMF · safety · imaging
AI Workforce
Supervised agents executing defined steps
Human Oversight
Validation & approval checkpoints
Built on four foundational pillars
Observability
Every agent step visible in real time across workflows.
Audit Trail
Immutable, timestamped record of every action and decision.
Explainability
Clear rationale for every output and recommendation.
Reproducibility
Identical inputs produce identical, verifiable results.
How it executes
Six steps. Every one, traceable.
A single, supervised execution loop — visible end to end.
Step 1
Observe Clinical Data
Monitor clinical systems, ingest data from protocols, queries, safety, documents and sites.
Step 2
Reason Across Context
Identify patterns, flag risks, draft outputs within approved workflow logic.
Step 3
Execute Approved Actions
Perform approved actions across defined workflows, under supervision.
Step 4
Validate with Human Review
Human reviewers retain approval authority where required.
Step 5
Log Every Decision
Every action documented with context and traceability.
Step 6
Escalate Exceptions
Exceptions are routed to the right human or function.
Who we support
The organizations executing modern clinical trials.
0+
Sponsors
Sponsor programs supported
0+
CROs
CRO partner workflows
0+
Site Networks
Sites in active operations
Trust & Compliance
Enterprise Security, Compliance and Governance
Maxis AI operates within the standards regulated clinical environments require — independently audited, fully traceable across every framework that matters.
ISO
ISO 9001:2015
Quality Management
Certified quality management system
ISO
ISO/IEC 27001:2022
Information Security
Information security management standard
Audit
SOC 2 Type II
Trust Services
Audited security, availability & confidentiality
Privacy
EU GDPR
Data Protection
European data privacy & protection compliance
GxP
GxP
Regulatory Practices
Good Practice frameworks across clinical ops
FDA
21 CFR Part 11
US FDA — eRecords
Electronic records & electronic signatures
FDA
21 CFR Part 820
US FDA — QSR
Quality System Regulation for medical software
Clinical
ICH-GCP
Good Clinical Practice
International clinical research standards
EU
EudraLex Annex 11
EU — Computerised Systems
EU GMP guidance for computerised systems
Key Safeguards
Controls built into every layer.
The execution model is governed by design — not bolted on after.
Role-based access controls
Granular permissions across teams and workflows
Encrypted data handling
At-rest and in-transit encryption end-to-end
Immutable audit logs
Every action recorded with full context
Inspection-ready traceability
Reviewable activity history on demand
Where AI Agents deliver impact
AI Workforce Use Cases Across Clinical Trials
Study Design & Feasibility
0%
Faster feasibility cycle
Clinical Data Management
0%
Faster query resolution
Clinical Trial Safety & PV
0%
Faster narrative drafting
Clinical Trial Risk & Quality
0%
Faster CAPA cycle
Clinical Trial Regulatory Submissions
0%
Faster eTMF QC
Site & Patient Ops
0%
Faster activation
Platform Architecture
Agentic AI Platform Architecture for Clinical Trials
Six purpose-built layers — scroll to see each one stack into place, from the agents you interact with down to the compliance foundation governing every action.
LAYER 01User Interface
Agent Layer
Purpose-built AI companions for specific clinical programming tasks.
Core Components
Clinical ProgrammingClinical Data ManagementRisk OversightRegulatory ComplianceGoverned Execution
LAYER 02Coordination
Orchestration Layer
Manages multi-agent workflows, task routing, and human-in-the-loop handoffs.
Core Components
Multi-Agent Workflow RoutingHuman-in-the-Loop HandoffsEscalation ManagementExecution State TrackingTask DecompositionSLA-Based Prioritization
LAYER 03Knowledge
Context Layer
Injects study-specific metadata, sponsor standards, and historical assets into prompts.
Core Components
Protocol IntelligenceStudy MetadataSponsor StandardsSOP & Work InstructionsHistorical Trial AssetsKnowledge Graph
LAYER 04Intelligence
Foundation Model Layer
Enterprise-grade LLMs fine-tuned for clinical data and SAS/R programming.
Core Components
Model-Agnostic AIClinical Language ModelsReasoning & GenerationModel RoutingModel EvaluationAI Safety Guardrails
LAYER 05Connectivity
Data Integration Layer
Secure connectors to clinical data repositories, metadata registries, and SCEs.
Core Components
Clinical System ConnectorsData InteroperabilitySecure Data ExchangeWorkflow Data IngestionMetadata HarmonizationReal-Time Data Signals
LAYER 06Foundation
Verification & Compliance Layer
Deterministic rules engine ensuring 21 CFR Part 11 compliance and zero hallucinations.
Core Components
SOC 2-Ready ControlsHIPAA-Aligned PrivacyGDPR GovernancePart 11 ReadinessGxP Validation
Testimonials
Loved by teams that scale fast.
Real stories from teams who streamlined workflows and delivered more with less.
Clinical trials are the engine of medical progress, but they are still built on slow, manual, and fragmented processes. The Maxis AI project shows that AI can be harnessed responsibly to modernize that engine — and we're just getting started.
Principal Investigator
Clinical Research Network
Customer testimonials
Principal Investigator — Clinical Research Network: Clinical trials are the engine of medical progress, but they are still built on slow, manual, and fragmented processes. The Maxis AI project shows that AI can be harnessed responsibly to modernize that engine — and we're just getting started.
CEO — Clinical Research Network: Building globally competitive clinical research in Africa requires more than infrastructure — it requires coordinated, scalable execution. Leveraging Maxis AI, we see a clear opportunity to streamline fragmented regulatory processes and accelerate trial starts.
Clinical Research Leader — Clinical Research Organization: Study startup has remained one of the most persistent bottlenecks in clinical trials. The real opportunity now lies in applying intelligent AI-driven workflows to remove operational friction and accelerate the path from protocol to patient.
Latest Resources
See all Insights from the frontline.
Blog
Clinical Trial Execution Gap: Why It's Now a Cost, Timeline, and Compliance Problem
Detection has never been better, yet timelines slip, deviations repeat, and costs compound. Inside the execution translation gap — and the supervised execution layer that closes it.
Read the full story
White Paper
A Governance Framework for Agentic AI in GxP Environments
Controls, audit trails, validation, and reviewer escalation across the agent lifecycle.
Read the full story
Case Study
Phase 1 Trial: Protocol to Database Lock in 9-12 months. Trial Cost Under $400K.
Maxis AI helped an oncology biotech accelerate study startup by 45% through one governed Phase 1 execution model - from protocol through database lock.
Read the full story
FAQ
All you need to know.
Common questions about deploying governed agentic AI in regulated clinical environments.
Agentic AI in Life Sciences refers to AI systems that can observe, reason, plan and execute regulated clinical workflows under governance controls and human oversight. Unlike traditional AI that primarily generates insights, Agentic AI completes structured operational work across clinical research, helping organizations increase execution capacity while maintaining compliance, auditability and regulatory readiness.
Maxis AI is the first AI Workforce for Clinical Trials. AI Workforce for Clinical Trials applies Agentic AI in Life Sciences to execute operational work rather than simply reporting on it. Governed AI agents perform structured tasks across study startup, clinical data management, statistical programming, medical writing, safety and quality while working within existing clinical systems and maintaining complete audit traceability.
Clinical trials generate more operational work than teams can manually resolve as studies become increasingly complex. The role of Agentic AI in Life Sciences is to execute structured, repeatable tasks such as query resolution, startup documentation, workflow coordination and medical writing support, enabling organizations to improve execution capacity without proportionally increasing headcount.
Agentic AI in Clinical Research improves trial execution by coordinating structured workflows across study startup, clinical operations, data management, safety, statistical programming and regulatory documentation. Governed AI agents execute approved tasks, escalate exceptions and support clinical teams with supervised execution, helping improve consistency, quality and delivery timelines.
Traditional AI solutions identify risks, generate predictions or surface insights, leaving clinical teams to complete the operational work. Maxis AI introduces an AI Workforce for Clinical Trials that executes approved workflows through governed AI agents. This supervised execution model helps organizations move beyond visibility by increasing operational throughput while maintaining compliance and human oversight.
Agentic AI in Life Sciences is designed for pharmaceutical companies, biotechnology organizations, contract research organizations (CROs), functional service providers and clinical trial site networks. It supports regulated functions including clinical operations, clinical data management, biostatistics, medical writing, safety, regulatory and quality where governance and audit-ready execution are essential.
No. Maxis AI is designed to augment clinical teams rather than replace them. The AI Workforce executes structured operational work within defined workflow boundaries while experts retain control over decisions, approvals and exceptions. Human validation checkpoints, governance policies and audit traceability ensure every workflow remains compliant, transparent and accountable.
Traditional AI helps clinical teams analyze information, generate content, or identify potential risks, but the responsibility for completing operational work still rests with people. An AI Workforce for Clinical Trials extends beyond recommendations by executing structured workflows under human supervision. Governed AI agents coordinate activities across study startup, clinical operations, data management, biometrics, medical writing, and quality while maintaining audit trails, validation checkpoints, and regulatory oversight.
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See how AI agents are transforming study startup, data management, oversight, and regulatory submissions.